使用BERT-Fused模型检测来自社交媒体的研究生心理压力
Muni Zhuang1,2, Dongsheng Cheng3, Xin Lu4
1National Institute for Data Science in Health and Medicine, Xiamen University, Xiamen, Fujian, China.
PloS one
|October 31, 2024
概括
检测研究生学生的压力至关重要. 一个新的BERT融合模型使用社交媒体数据进行准确,经济高效的心理压力检测,优于旧方法.
科学领域:
- 人工智能的人工智能
- 计算社会科学 计算社会科学
- 心理健康技术 心理健康技术
背景情况:
- 研究生经历了重要的压力因素,增加了焦虑,抑郁和自杀念头的风险.
- 目前的压力检测方法通常是复杂和昂贵的,依赖于心理尺度或专用设备.
- 迫切需要可访问和高效的工具来早期识别和解决学生的心理压力.
研究的目的:
- 开发和评估一种新的BERT融合模型,用于使用社交媒体数据自动检测研究生心理压力.
- 通过提供具有成本效益和可扩展的解决方案来改进现有的压力检测方法.
- 通过提供精细的心理压力概况,使及时和有针对性的干预成为可能.
主要方法:
- 利用改进的BERT-LDA特征提取算法,在大型社交媒体数据集中识别群体压力指标.
- 集成了一个BiLSTM-CRF命名实体识别模型,以构建多维心理压力配置文件.
- 通过融合多维数据来分析细粒度特征表示.
主要成果:
- 拟议的BERT融合模型实现了高性能指标:92.55%的准确性,93.47%的回忆和92.18%的F1分数.
- 该模型在所有三种实体类型中都表现出强的表现,F1得分超过89%.
- 与BERT融合的模型在应力检测方面明显优于BiLSTM等传统模型.
结论:
- 开发的BERT融合模型提供了一种有效和自动化的方法,可以通过社交媒体检测研究生心理压力.
- 这项研究为大学实施积极的心理健康监测和支持系统提供了理论和实践框架.
- 这些发现突显了社交媒体数据分析在早期识别和干预学生心理困扰方面的潜力.
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